Chart Engine Indexlet Segmentation for Query Performance
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Solution Overview
Problem
Current data management and analysis systems face challenges in efficiently processing and aggregating data across multiple dimensions and tables, particularly in handling large datasets and complex queries.
Innovation Solution
The proposed system employs a method that involves receiving an expression involving one or more dimensions, determining associated indexlets, querying these indexlets, aggregating the results, and generating graphical objects based on hypercubes created from the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed and aggregated across multiple dimensions and tables using traditional methods, then comprehensive analysis is achieved, but processing efficiency deteriorates and query performance decreases
Solution Approach 1:
The patent divides the data processing system into multiple independent indexlets, each responsible for specific dimensions and tables. This segmentation allows parallel processing of different data subsets, improving overall processing efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing data into indexlets with hierarchical relationships. This additional organizational dimension enables efficient navigation and aggregation across multiple dimensions without requiring complex full-table scans, thus improving productivity without proportionally increasing system complexity.
2Measurement precision
If traditional data aggregation methods are used across large datasets, then complete results are obtained, but query execution time increases
Solution Approach 1:
The patent pre-computes and stores aggregated results in indexlets before queries are executed. This preliminary action allows the system to retrieve pre-aggregated data rather than computing it on-demand, significantly reducing query execution time while maintaining result accuracy through the use of deterministic aggregation functions.
Solution Approach 2:
The patent applies different aggregation strategies to different indexlets based on their specific characteristics and the query requirements. This local optimization ensures that each indexlet contributes accurately to the final result while using the most efficient aggregation method for its data type, maintaining overall query accuracy while minimizing execution time.
3Adaptability or versatility
If complex queries involving multiple dimensions are processed, then comprehensive analysis is achieved, but processing speed decreases
Solution Approach 1:
The patent segments complex queries into smaller sub-queries that can be executed in parallel across different indexlets. This segmentation maintains the versatility to handle complex multi-dimensional queries while improving processing speed through concurrent execution of independent query components.
Solution Approach 2:
The patent merges results from multiple indexlets through efficient aggregation operations. This combining process maintains comprehensive analysis capability by integrating results from all relevant indexlets while using optimized merge algorithms to minimize the time required to consolidate results from parallel query executions.
Data Source
AI summary
Systems and methods for processing queries are described herein. As an example, a query may comprise an expression. Based on the expression, one or more indexlets may be determined. Using the one or more indexlets, a result of the expression may be determined.